I frequently work with private data. Sometimes, it lives on my personal machine rather than on a database server. Sometimes, even if it lives on a remote database server, it is better that I use locally cached data than query the database each time I want to do analysis on the data set. I have always dealt with this by creating encrypted disk images with secure passwords (stored in 1Password). This is a nice extra layer of protection for private data served on a laptop, and it adds little complication to my workflow. I just have to remember to mount and unmount the disk images.

However, it can be inconvenient from a project perspective to refer to data in a distant location like /Volumes/ClientData/Entity/facttable.csv. In most cases, I would prefer the data “reside” in data/ or cache/ “inside” of my project directory.

Luckily, there is a great way that allows me to point to data/facttable.csv in my R code without actually having facttable.csv reside there: symlinking.

A symlink is a symbolic link file that sits in the preferred location and references the file path to the actual file. This way, when I refer to data/facttable.csv the file system knows to direct all of that activity to the actual file in /Volumes/ClientData/Entity/facttable.csv.

From the command line, a symlink can be generated with a simple command:

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ln -s target_path link_path

R offers a function that does the same thing:

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file.symlink(target_path, link_path)

where target_path and link_path are both strings surrounded by quotation marks.

One of the first things I do when setting up a new analysis is add common data storage file extensions like .csv and .xls to my .gitignore file so that I do not mistakenly put any data in a remote repository. The second thing I do is set up symlinks to the mount location of the encrypted data.